Abstract Cyberthreat intelligence (CTI) reports on past cyberattacks describe the sequence of actions of attackers in terms of time. The sequence contains temporal relations among attack actions, such as a malware is first downloaded and then executed. Information related to temporal relations enables cybersecurity practitioners to investigate past cyberattack incidents and analyze attackers’ behavior. However, cybersecurity practitioners must extract such information automatically, in a structured manner, through a common vocabulary to reduce human effort and enable sharing, and collaboration. The goal of this paper is to aid security practitioners in proactive defense against attacks by automatic information extraction of temporal relations among attack actions from cyberthreat intelligence reports. We propose ChronoCTI, an automated pipeline for extracting temporal relations among attack actions from CTI reports. The attack actions are represented as MITRE ATT&CK techniques, and the relations are represented as a knowledge graph. To construct ChronoCTI, we build a ground truth dataset of temporal relations and apply large language models, natural language processing, and machine learning techniques. ChronoCTI demonstrates higher precision but lower recall performance on a real-world dataset of 94 CTI reports. We apply ChronoCTI on a set of 713 CTI reports, where we identify 9 categories of temporal attack patterns consisting of 124 temporal attack patterns. We identify that the most prevalent pattern category is to trick victim users into executing malicious code to initiate the attack, followed by bypassing the anti-malware system in the victim software systems. Based on the observed patterns, we advocate for training users about cybersecurity best practices, introducing appropriate warning messages for end-users, introducing immutable operating systems, and enforcing multi-user authentications. Moreover, we advocate that practitioners leverage the automated mining capability of ChronoCTI and design countermeasures against recurring attack patterns.

Cyberthreat intelligence (CTI) reports on past cyberattacks describe the sequence of actions of attackers in terms of time. The sequence contains temporal relations among attack actions, such as a malware is first downloaded and then executed. Information related to temporal relations enables cybersecurity practitioners to investigate past cyberattack incidents and analyze attackers' behavior. However, cybersecurity practitioners must extract such information automatically, in a structured manner, through a common vocabulary to reduce human effort and enable sharing, and collaboration. The goal of this paper is to aid security practitioners in proactive defense against attacks by automatic information extraction of temporal relations among attack actions from cyberthreat intelligence reports. We propose ChronoCTI, an automated pipeline for extracting temporal relations among attack actions from CTI reports. The attack actions are represented as MITRE ATT&CK techniques, and the relations are represented as a knowledge graph. To construct ChronoCTI, we build a ground truth dataset of temporal relations and apply large language models, natural language processing, and machine learning techniques. ChronoCTI demonstrates higher precision but lower recall performance on a real-world dataset of 94 CTI reports. ChronoCTI achieves macro precision, recall, and F1 scores of 0.75, 0.46, and 0.54, respectively. ChronoCTI aids practitioners in analyzing large volumes of CTI reports, thinking like attackers, and knowing what attack actions are likely to happen next, which enables the practitioners to assess imminent threats and strengthen their cybersecurity readiness.

The prevalent use of third-party components in modern software development, rapid modernization, and digitization have significantly amplified the risk of software supply chain attacks. Popular large registries like npm and PyPI are highly targeted malware distribution channels for attackers due to heavy growth and dependence on third-party components. Industry and academia are working towards building tools to detect malware in the software supply chain. However, a lack of benchmark datasets containing both malicious and neutral packages hampers the evaluation of the performance of these malware detection tools. The goal of our study is to aid researchers and tool developers in evaluating and improving malware detection tools by contributing a benchmark dataset built by systematically collecting malicious and neutral packages from the npm and PyPI ecosystems. We present MalwareBench, a labeled dataset of 20,792 packages (of which 6,659 are malicious) from the npm and PyPI ecosystems. We constructed the benchmark dataset by incorporating pre-existing malware datasets with the Socket internal benchmark data and including popular and newly released npm and PyPI packages. The ground truth labels of these neutral packages were determined using the Socket AI Scanner and manual inspection.

According to GitGuardian’s monitoring of public GitHub repositories, the exposure of secrets (API keys and other credentials) increased two-fold in 2021 compared to 2020, totaling more than six million secrets. However, no benchmark dataset is publicly available for researchers and tool developers to evaluate secret detection tools that produce many false positive warnings. The goal of our paper is to aid researchers and tool developers in evaluating and improving secret detection tools by curating a benchmark dataset of secrets through a systematic collection of secrets from open-source repositories. We present a labeled dataset of source codes containing 97,479 secrets (of which 15,084 are true secrets) of various secret types extracted from 818 public GitHub repositories. The dataset covers 49 programming languages and 311 file types.

Background: Machine learning techniques have been widely used and demonstrate promising performance in many software security tasks such as software vulnerability prediction. However, the class ratio within software vulnerability datasets is often highly imbalanced (since the percentage of observed vulnerability is usually very low). Goal: To help security practitioners address software security data class imbalanced issues and further help build better prediction models with resampled datasets. Method: We introduce an approach called Dazzle which is an optimized version of conditional Wasserstein Generative Adversarial Networks with gradient penalty (cWGAN-GP). Dazzle explores the architecture hyperparameters of cWGAN-GP with a novel optimizer called Bayesian Optimization. We use Dazzle to generate minority class samples to resample the original imbalanced training dataset. Results: We evaluate Dazzle with three software security datasets, i.e., Moodle vulnerable files, Ambari bug reports, and JavaScript function code. We show that Dazzle is practical to use and demonstrates promising improvement over existing state-of-the-art oversampling techniques such as SMOTE (e.g., with an average of about 60% improvement rate over SMOTE in recall among all datasets). Conclusion: Based on this study, we would suggest the use of optimized GANs as an alternative method for security vulnerability data class imbalanced issues.

Static analysis tool alerts can help developers detect potential defects in the code early in the development cycle. However, developers are not always able to respond to the alerts with their preferred action and may turn away from using the tool. In this paper, we qualitatively analyze 280 Stack Overflow (SO) questions regarding static analysis tool alerts to identify the challenges developers face in understanding and responding to these alerts. We find that the most prevalent question on SO is how to ignore and filter alerts, followed by validation of false positives. Our findings confirm prior researchers' findings related to notification communication theory as 44.6% of the SO questions that we analyzed indicate developers face communication challenges.